Switching devices (hereinafter switches) are used in power distribution networks to isolate faults and restore power, following the occurrence of an abnormality or fault. Switches may include, for example, circuit breakers, reclosers and sectionalizers. The identification of proper switches for fault isolation is relatively easy, whereas the determination of switches and switching sequences for power restoration can be quite complex. Depending on pre-fault network topology, many alternative paths (back-feeding pathways) may be available from which power can flow from an alternative power source, through a series of switches, to one or more disconnected loads. Determining the proper back-feeding pathway is contingent on the available capacities of the back-feed sources, as well as the power handling capacities of the intermediate devices (e.g. power lines, reclosers, switches, and transformers).
Intelligent algorithms may be executed on computer systems to choose the optimal restoration path. These algorithms scan the available back-feed paths and identify those that are capable of providing the additional loads without exceeding the capability limits of the alternate source. Further, the algorithms may check the thermal and other limits of the intermediate lines and devices to ensure safe back-feed operation. Paths that satisfy all such capacity checks are labeled as feasible paths for back-feed. If multiple feasible paths are available for back-feeding of one group of disconnected loads, the best option may be selected. For instance, a best option may be the one that results in the lowest loading of the alternate source. The intelligent algorithms also attempt to ensure that the approved back-feed solution is reliable, will restore the unserved loads without interrupting power to other loads, and that the back-feed network operates safely.
As should be apparent, evaluating alternative back-feed sources requires some knowledge of the loads (e.g. Amps, MW or MVar power) on the network. One method of estimating loads is to simply use the static rated value of the load. The advantage of this procedure is that it is simple and can be programmed offline, as the load rating is known from the network configuration data. In many instances however this procedure may not be sufficient, as the actual load varies with time and may deviate considerably from its rated value. The load rating may also vary from season to season or even from day time to night time. As an example, if a particular load is an industrial facility, a restoration may be carried out at midnight when the rated load demands are low (e.g. 200 A). Given that the load is industrial load, the loads will likely be significantly higher 12 hours later, and the new, larger load may possibly overload the alternative source. If, for example, the rated demand increased to 350 amps during the day, this increase could cause the source to overload. The overloading may trigger further undesirable consequences, such as cascade tripping events and the loss of other sensitive loads. The variation in the load should be taken into consideration while performing the back-feed so that future unwanted consequences are avoided.
Thus, there is a need in the art for a system and method that forecasts loads on a network in an adaptive manner for use in back-feed capacity determinations.